CN113010425B - System quality monitoring method, device, computer equipment and storage medium - Google Patents
System quality monitoring method, device, computer equipment and storage medium Download PDFInfo
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- 238000004364 calculation method Methods 0.000 claims abstract description 77
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
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- G—PHYSICS
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Abstract
The application relates to a data processing technology, and provides a system quality monitoring method, a device, computer equipment and a storage medium, which comprise the following steps: constructing an initial test template according to the target parameters; determining calculation logic among target parameters, and adjusting an initial test template to obtain a target test template; generating a multiplexing test case, and calling a target test template to execute the multiplexing test case to obtain a system test expected value; calling a system to execute a multiplexing test case to obtain a system test true value; performing curve fitting according to the expected value of the system test to obtain a first execution curve, and performing curve fitting according to the true value of the system test to obtain a second execution curve; calculating the difference degree between the first execution curve and the second execution curve; and determining the system quality of the target system according to the difference degree. The intelligent city intelligent monitoring system can improve efficiency and accuracy of system quality monitoring and promote rapid development of intelligent cities.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a system quality monitoring method, a device, a computer device, and a storage medium.
Background
In the software development life cycle, software Quality Assurance (QA) is an important point of passing through the whole development process of qualified software, and software testing is a main means of Quality Assurance (QA).
In carrying out the present invention, the applicant has found that the following technical problems exist in the prior art: in the current system quality test, on the test range, only aiming at the most main algorithm branch design cases of the algorithm, the coverage range of the test cases is low, and the algorithm is only verified in the limited branches, so that the software quality can not be comprehensively ensured; in the test method, the manual analysis system returns json to be compared with the template calculated manually in advance, so that time and labor are consumed, and the data are numerous and are easy to make mistakes manually.
Therefore, it is necessary to provide a system quality monitoring method, which can improve the efficiency and accuracy of system quality monitoring.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a system quality monitoring method, a system quality monitoring device, a computer device, and a storage medium, which can improve the efficiency and accuracy of system quality monitoring.
An embodiment of the present application provides a system quality monitoring method, where the system quality monitoring method includes:
Acquiring and analyzing preset information in a target system to obtain target parameters, and constructing an initial test template according to the target parameters;
determining calculation logic among the target parameters, and adjusting the initial test template according to the calculation logic to obtain a target test template;
generating a multiplexing test case, and calling the target test template to execute the multiplexing test case to obtain a system test expected value;
calling the target system to execute the multiplexing test case to obtain a system test true value;
performing curve fitting according to the system test expected value to obtain a first execution curve, and performing curve fitting according to the system test true value to obtain a second execution curve;
calculating the difference degree between the first execution curve and the second execution curve;
and determining the system quality of the target system according to the difference degree.
Further, in the method for monitoring system quality provided in the embodiment of the present application, the obtaining and analyzing preset information in the target system, and obtaining the target parameter includes:
analyzing the preset information to obtain a target benefit item;
acquiring initial parameters corresponding to the target benefit items;
And screening and deleting repeated parameter values in the initial parameters to obtain target parameters.
Further, in the method for monitoring system quality provided in the embodiment of the present application, the constructing an initial test template according to the target parameter includes:
defining a parameter layout in an initial test template;
determining attribute values of the target parameters in the parameter layout;
and constructing an initial test template according to the parameter layout and the attribute values.
Further, in the above system quality monitoring method provided in the embodiment of the present application, the method further includes:
acquiring a target benefit item corresponding to preset information;
splitting the target interest item to obtain a plurality of calculation factors;
determining weight items and parameter items corresponding to the calculation factors;
and analyzing the parameter items to obtain calculation logic among all target parameters.
Further, in the method for monitoring system quality provided in the embodiment of the present application, the adjusting the initial test template according to the calculation logic to obtain the target test template includes:
acquiring calculation logic among the target parameters;
establishing a data link between the target parameters according to the calculation logic;
And adding the data link in the initial test template to obtain a target test template.
Further, in the above system quality monitoring method provided by the embodiment of the present application, the generating a multiplexing test case includes:
acquiring a preset test case library, and selecting a public test case from the preset test case library;
determining a target test point corresponding to the target system, and acquiring a test requirement document corresponding to the target test point;
generating a non-public test case corresponding to the target test point according to the test requirement document and a preset test case template;
and combining the public test case and the non-public test case to obtain a multiplexing test case.
Further, in the above system quality monitoring method provided by the embodiment of the present application, the calculating a degree of difference between the first execution curve and the second execution curve includes:
acquiring first trend data of the first execution curve in a preset stage;
acquiring second trend data of the second execution curve in the preset stage;
determining difference data between the first trend data and the second trend data;
and calculating the data quantity of the difference data, traversing the preset mapping relation between the data quantity and the difference degree according to the data quantity, and obtaining the difference degree corresponding to the data quantity.
The second aspect of the embodiments of the present application further provides a system quality monitoring device, where the system quality monitoring device includes:
the parameter acquisition module is used for acquiring and analyzing preset information in a target system to obtain target parameters, and constructing an initial test template according to the target parameters;
the template adjustment module is used for determining calculation logic among the target parameters and adjusting the initial test template according to the calculation logic to obtain a target test template;
the expected value acquisition module is used for generating a multiplexing test case, calling the target test template to execute the multiplexing test case and obtaining a system test expected value;
the true value acquisition module is used for calling the target system to execute the multiplexing test case to obtain a system test true value;
the curve fitting module is used for performing curve fitting according to the system test expected value to obtain a first execution curve, and performing curve fitting according to the system test true value to obtain a second execution curve;
the difference calculation module is used for calculating the difference degree between the first execution curve and the second execution curve;
and the quality determining module is used for determining the system quality of the target system according to the difference degree.
A third aspect of the embodiments of the present application further provides a computer device, the computer device including a processor configured to implement a system quality monitoring method according to any one of the preceding claims when executing a computer program stored in a memory.
The fourth aspect of the embodiments of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the system quality monitoring method according to any one of the foregoing aspects.
According to the system quality monitoring method, the system quality monitoring device, the computer equipment and the computer readable storage medium, the target test template is constructed according to the target parameters and the calculation logic among the target parameters, the target test template is called to execute the test case, and the system test expected value is obtained; in addition, when the test cases are designed, a mode of generating the multiplexed test cases is adopted, so that compared with the mode of independently designing the whole set of test cases aiming at a target system, the design time of the test cases can be reduced, and the efficiency of monitoring the system quality is improved; in addition, the method and the device compare the difference degree between the first execution curve and the second execution curve in a curve fitting mode, and determine the system quality of the target system according to the difference degree, so that the system quality of the target system can be intuitively obtained. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs and intelligent traffic, for example, the intelligent government affairs can be promoted to develop rapidly based on a system quality monitoring module of the intelligent government affairs.
Drawings
Fig. 1 is a flowchart of a system quality monitoring method according to an embodiment of the present application.
Fig. 2 is a block diagram of a system quality monitoring device according to a second embodiment of the present application.
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application.
The following detailed description will further illustrate the application in conjunction with the above-described figures.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The system quality monitoring method provided by the embodiment of the invention is executed by the computer equipment, and correspondingly, the system quality monitoring device is operated in the computer equipment.
Fig. 1 is a flowchart of a system quality monitoring method according to a first embodiment of the present application. As shown in fig. 1, the system quality monitoring method is used for monitoring the system quality of the target system, and the system quality monitoring method may include the following steps, where the order of the steps in the flowchart may be changed according to different requirements, and some may be omitted:
s11, acquiring and analyzing preset information in a target system to obtain target parameters, and constructing an initial test template according to the target parameters.
In at least one embodiment of the present application, the target system may be a policy profit system that may be used to calculate the value of each benefit of the policy. For the insurance policy of different dangerous types, the corresponding benefit items can be the same or different. The preset information may include a policy number, a policy risk, a policy period, and various information such as an applicant's age, physical condition, working condition, and occupation category, and in order to ensure confidentiality and privacy of the policy information, the preset information may be stored in a target node of the blockchain. The target parameters are parameters of monitoring results affecting the system quality in the policy information, and the target parameters can be preset in a log by a system staff or can be obtained by a machine learning mode.
In an embodiment, when the target parameter is preset in the log by the system personnel, optionally, obtaining the target parameter may include:
obtaining a target log;
detecting whether the target log contains identification information corresponding to the target parameter or not;
and when the detection result is that the target log contains the identification information corresponding to the target parameter, determining the content of the position corresponding to the identification information as the target parameter.
The identification information is used for identifying the target parameter, the identification information can be a numerical identification, a letter identification and the like, and the target parameter can be structured data.
In another embodiment, when the target parameter is obtained by machine learning, optionally, the obtaining and analyzing the preset information in the target system may further include:
analyzing the preset information to obtain a target benefit item;
acquiring initial parameters corresponding to the target benefit items;
and screening and deleting repeated parameter values in the initial parameters to obtain target parameters.
The number of the target benefit items can be 1 or more. For each target benefit, the corresponding initial parameter is used to calculate the value of the target benefit. Initial parameters corresponding to different benefit items may be the same, so that the initial parameters need to be screened to perform deduplication processing on the initial parameters to obtain target parameters.
Optionally, the constructing an initial test template according to the target parameters may include:
defining a parameter layout in an initial test template;
determining attribute values of the target parameters in the parameter layout;
and constructing an initial test template according to the parameter layout and the attribute values.
The parameter layout refers to an arrangement manner of target parameters preset in the initial test template, for example, the target parameters may be arranged in a random manner, or the target parameters may be arranged in a manner of byte length of the parameters, which is not limited herein. The attribute value may refer to a data length, a data type, etc. of the target parameter, which is not limited herein.
S12, determining calculation logic among the target parameters, and adjusting the initial test template according to the calculation logic to obtain a target test template.
In at least one embodiment of the present application, the calculation logic is configured to calculate a value of a target benefit item of a corresponding policy according to the target parameter. Before determining the calculation logic between the target parameters, the method needs to establish the corresponding relation between the target parameters and the calculation logic in advance.
Optionally, the establishing the correspondence between the target parameter and the calculation logic may include:
acquiring a target benefit item corresponding to preset information;
splitting the target interest item to obtain a plurality of calculation factors;
determining weight items and parameter items corresponding to the calculation factors;
and analyzing the parameter items to obtain calculation logic among all target parameters.
Because the target benefit items corresponding to different policy information may be different, different calculation logics exist among different target parameters, and the calculation logics of the target parameters can be determined according to the corresponding relations by establishing the corresponding relations between the target parameters and the calculation logics, so that the accuracy of the determination of the calculation logics is improved, and the accuracy of the quality monitoring of the system is further improved. The target benefit item may be composed of a plurality of calculation factors, wherein the calculation factors refer to algorithm factors with single calculation logic, the calculation factors may include weight items and parameter items, the weight items are used for identifying importance degrees of the parameter items, the parameter items may be parameter items obtained by processing one target parameter by a target function, and the parameter items may also be parameter items obtained by processing a plurality of target parameters by the target function. When the parameter item is obtained by processing a plurality of target parameters by the target function, analyzing the parameter item, and converting the parameter item into data in a preset data format, wherein the data in the preset data format is the calculation logic among the target parameters. The preset data format refers to a preset data format, for example, the preset data format may be { objective function, objective parameter 1, objective parameter 2, …, objective parameter n }, which is not limited herein.
Optionally, the adjusting the initial test template according to the calculation logic to obtain the target test template may include:
acquiring calculation logic between target parameters corresponding to the target benefit items;
establishing a data link between the target parameters according to the calculation logic;
and adding the data link in the initial test template to obtain a target test template.
The data link may be a formula link established according to calculation logic between the target parameters, and the formula link is added to the target parameters in the initial test template, so that each target parameter calculates each benefit item according to the formula link, thereby facilitating execution of the test case.
S13, generating a multiplexing test case, and calling the target test template to execute the multiplexing test case to obtain a system test expected value.
In at least one embodiment of the present application, a test case is a set of test data and procedures that determine the most likely error to be found, enabling the system to test a certain function. By multiplexing the test cases, the test efficiency can be improved, and the system quality monitoring efficiency can be further improved.
Optionally, the generating the multiplexing test case may include:
Acquiring a preset test case library, and selecting a public test case from the preset test case library;
determining a target test point corresponding to the target system, and acquiring a test requirement document corresponding to the target test point;
generating a non-public test case corresponding to the target test point according to the test requirement document and a preset test case template;
and combining the public test case and the non-public test case to obtain a multiplexing test case.
The preset test case library comprises a plurality of test cases required by testing other non-target systems, the plurality of test cases can be divided into a public part and a non-public part according to functions, and preset marks are respectively added to the public part and the non-public part, wherein the preset marks can be digital marks, letter marks or color marks, and the other non-target systems are similar to the functional modules contained in the target systems. And selecting a public part from the preset test case library as a public test case according to the preset mark. The target test points are interfaces which are preset and need to be tested by the target system, and for each target test point, a test requirement document exists. The test requirement document is a document preset by a system staff and containing test basic information, and a test requirement data list corresponding to the target test point can be extracted by analyzing the test requirement document. The non-public test cases refer to the cases designed for the target test points independently, the test points of different target systems are different, and the corresponding non-public test cases can be different. The non-public test case can be obtained by adjusting related data in the test case template according to the test requirement document. And the corresponding relation exists between the test requirement data list in the test requirement document and the related data in the test case template, and the related data in the test case template can be adjusted to the test requirement data list by determining the corresponding relation.
Optionally, the calling the target test template to execute the multiplexing test case may include:
determining target parameters in the target test template;
selecting a parameter value corresponding to the target parameter in the multiplexing test case;
and calling calculation logic in the target test template to execute the parameter value to obtain a system test expected value.
S14, calling the target system to execute the multiplexing test case to obtain a system test true value.
In at least one embodiment of the present application, the target system is called to execute the multiplexing test case, so as to obtain a system test actual value, and when the system test expected value and the system test actual value have large differences, it is determined that the target system has more defects and the system quality is poor by comparing the system test expected value and the system test actual value; and when the expected value of the system test is not different from the actual value of the system test, and the goods difference is small, the target system is determined to have fewer defects, and the system quality is better.
S15, performing curve fitting according to the system test expected values to obtain a first execution curve, and performing curve fitting according to the system test actual values to obtain a second execution curve.
In at least one embodiment of the present application, curve fitting is performed according to the expected value of the system test to obtain a first execution curve, and curve fitting is performed according to the actual value of the system test to obtain a second execution curve, so that the difference between the expected value of the system test and the actual value of the system test can be more intuitively seen by comparing the first execution curve with the second execution curve, thereby improving the quality monitoring efficiency of the system.
Optionally, performing curve fitting according to the expected values of the system test, and obtaining a first execution curve may include:
determining a preset function according to the expected value of the system test;
and calling the preset function to process the expected value of the system test to obtain a first execution curve.
The preset function is a preset function for determining a first execution curve. By adopting the method, curve fitting can be carried out according to the system test true value, and a second execution curve is obtained.
S16, calculating the difference degree between the first execution curve and the second execution curve.
In at least one embodiment of the present application, calculating the difference between the first execution curve and the second execution curve may be accomplished by calculating the difference between trend data of two curves, where the trend data may refer to a slope of the corresponding execution curve.
Optionally, the calculating the degree of difference between the first execution curve and the second execution curve may include:
acquiring first trend data of the first execution curve in a preset stage;
acquiring second trend data of the second execution curve in the preset stage;
determining difference data between the first trend data and the second trend data;
and calculating the data quantity of the difference data, traversing the preset mapping relation between the data quantity and the difference degree according to the data quantity, and obtaining the difference degree corresponding to the data quantity.
The trend data may include a slope of a corresponding execution curve, and the preset phase may include three phases, which are respectively: smoking phase, SIT phase and regression phase. In the same phase, the slope variation ranges of the second execution curve and the first execution curve should be consistent. By dividing the execution curve according to the preset stage, the trend detection of the execution curve with the same slope change range is realized, and the monitoring efficiency of the execution curve is improved.
S17, determining the system quality of the target system according to the difference degree.
In at least one embodiment of the present application, the system quality may be sequentially classified into a class a, a class B and a class C according to the quality, where when the class of the system quality is a class a, it indicates that the system is defect-free, and the system quality is high; when the level of the system quality is B level, the system is indicated to have partial defects, and the system quality is medium; when the level of the system quality is C level, the system is indicated to have more defects and has low quality. For each level, there is a range of variability corresponding thereto. Determining the system quality of the target system according to the difference, namely determining a difference range corresponding to the target system according to the difference; and determining the system quality of the target system according to the difference degree range.
According to the system quality monitoring method provided by the embodiment of the application, the target test template is constructed according to the target parameters and the calculation logic among the target parameters, and the target test template is called to execute the test case, so that the system test expected value is obtained, and compared with the prior art, when the test case is designed, the test expected value is manually determined, the design error caused by manual design can be avoided, the correctness of the test expected value is ensured, and the accuracy of system quality monitoring is further improved; in addition, when the test cases are designed, a mode of generating the multiplexed test cases is adopted, so that compared with the mode of independently designing the whole set of test cases aiming at a target system, the design time of the test cases can be reduced, and the efficiency of monitoring the system quality is improved; in addition, the method and the device compare the difference degree between the first execution curve and the second execution curve in a curve fitting mode, and determine the system quality of the target system according to the difference degree, so that the system quality of the target system can be intuitively obtained. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs and intelligent traffic, for example, the intelligent government affairs can be promoted to develop rapidly based on a system quality monitoring module of the intelligent government affairs.
Fig. 2 is a block diagram of a system quality monitoring device according to a second embodiment of the present application.
In some embodiments, the system quality monitoring device 20 may include a plurality of functional modules consisting of computer program segments. The computer program of the individual program segments in the system quality monitoring apparatus 20 may be stored in a memory of a computer device and executed by at least one processor to perform the functions of system quality monitoring (described in detail with reference to fig. 1).
In this embodiment, the system quality monitoring device 20 may be divided into a plurality of functional modules according to the functions performed by the system quality monitoring device. The functional module may include: a parameter acquisition module 201, a template adjustment module 202, an expected value acquisition module 203, a true value acquisition module 204, a curve fitting module 205, a variance calculation module 206, and a quality determination module 207. A module as referred to in this application refers to a series of computer program segments, stored in a memory, capable of being executed by at least one processor and of performing a fixed function. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The parameter obtaining module 201 may be configured to obtain and parse preset information in a target system to obtain target parameters, and construct an initial test template according to the target parameters.
In at least one embodiment of the present application, the target system may be a policy profit system that may be used to calculate the value of each benefit of the policy. For the insurance policy of different dangerous types, the corresponding benefit items can be the same or different. The preset information may include a policy number, a policy risk, a policy period, and various information such as an applicant's age, physical condition, working condition, and occupation category, and in order to ensure confidentiality and privacy of the policy information, the preset information may be stored in a target node of the blockchain. The target parameters are parameters of monitoring results affecting the system quality in the policy information, and the target parameters can be preset in a log by a system staff or can be obtained by a machine learning mode.
In an embodiment, when the target parameter is preset in the log by the system personnel, optionally, obtaining the target parameter may include:
obtaining a target log;
detecting whether the target log contains identification information corresponding to the target parameter or not;
and when the detection result is that the target log contains the identification information corresponding to the target parameter, determining the content of the position corresponding to the identification information as the target parameter.
The identification information is used for identifying the target parameter, the identification information can be a numerical identification, a letter identification and the like, and the target parameter can be structured data.
In another embodiment, when the target parameter is obtained by machine learning, optionally, the obtaining and analyzing the preset information in the target system may further include:
analyzing the preset information to obtain a target benefit item;
acquiring initial parameters corresponding to the target benefit items;
and screening and deleting repeated parameter values in the initial parameters to obtain target parameters.
The number of the target benefit items can be 1 or more. For each target benefit, the corresponding initial parameter is used to calculate the value of the target benefit. Initial parameters corresponding to different benefit items may be the same, so that the initial parameters need to be screened to perform deduplication processing on the initial parameters to obtain target parameters.
Optionally, the constructing an initial test template according to the target parameters may include:
defining a parameter layout in an initial test template;
determining attribute values of the target parameters in the parameter layout;
And constructing an initial test template according to the parameter layout and the attribute values.
The parameter layout refers to an arrangement manner of target parameters preset in the initial test template, for example, the target parameters may be arranged in a random manner, or the target parameters may be arranged in a manner of byte length of the parameters, which is not limited herein. The attribute value may refer to a data length, a data type, etc. of the target parameter, which is not limited herein.
The template adjustment module 202 may be configured to determine calculation logic between the target parameters, and adjust the initial test template according to the calculation logic to obtain a target test template.
In at least one embodiment of the present application, the calculation logic is configured to calculate a value of a benefit item of the corresponding policy according to the target parameter. Before determining the calculation logic between the target parameters, the method needs to establish the corresponding relation between the target parameters and the calculation logic in advance.
Optionally, the establishing the correspondence between the target parameter and the calculation logic may include:
acquiring a target benefit item corresponding to preset information;
splitting the target interest item to obtain a plurality of calculation factors;
Determining weight items and parameter items corresponding to the calculation factors;
and analyzing the parameter items to obtain calculation logic among all target parameters.
Because interest items corresponding to different policy information may be different, different calculation logics exist among different target parameters, and by establishing a corresponding relation between the target parameters and the calculation logics, the calculation logics of the target parameters can be determined according to the corresponding relation, so that the accuracy of determination of the calculation logics is improved, and the accuracy of monitoring the system quality is further improved. The target benefit item consists of a plurality of calculation factors, wherein the calculation factors refer to algorithm factors with single calculation logic, the calculation factors comprise weight items and parameter items, the weight items are used for identifying the importance degree of the parameter items, the parameter items can be obtained by processing one target parameter by a target function, and the parameter items can also be obtained by processing a plurality of target parameters by the target function. When the parameter item is obtained by processing a plurality of target parameters by the target function, analyzing the parameter item, and converting the parameter item into data in a preset data format, wherein the data in the preset data format is the calculation logic among the target parameters. The preset data format refers to a preset data format, for example, the preset data format may be { objective function, objective parameter 1, objective parameter 2, …, objective parameter n }.
Optionally, the adjusting the initial test template according to the calculation logic to obtain the target test template may include:
acquiring calculation logic between target parameters corresponding to the target benefit items;
establishing a data link between the target parameters according to the calculation logic;
and adding the data link in the initial test template to obtain a target test template.
The data link may be a formula link established according to calculation logic between the target parameters, and the formula link is added to the target parameters in the initial test template, so that each target parameter calculates each benefit item according to the formula link, thereby facilitating execution of the test case.
The expected value obtaining module 203 may be configured to generate a multiplexed test case, and call the target test template to execute the multiplexed test case, so as to obtain a system test expected value.
In at least one embodiment of the present application, a test case is a set of test data and procedures that determine the most likely error to be found, enabling the system to test a certain function. By multiplexing the test cases, the efficiency of the test process can be improved, and the quality monitoring efficiency of the system can be further improved.
Optionally, the generating the multiplexing test case may include:
acquiring a preset test case library, and selecting a public test case from the preset test case library;
determining a target test point corresponding to the target system, and acquiring a test requirement document corresponding to the target test point;
generating a non-public test case corresponding to the target test point according to the test requirement document and a preset test case template;
and combining the public test case and the non-public test case to obtain a multiplexing test case.
The preset test case library comprises a plurality of test cases required by testing other non-target systems, the plurality of test cases can be divided into a public part and a non-public part according to functions, and preset marks are respectively added to the public part and the non-public part, wherein the preset marks can be digital marks, letter marks or color marks, and the other non-target systems are similar to the functional modules contained in the target systems. And selecting a public part from the preset test case library as a public test case according to the preset mark. The target test points are interfaces which are preset and need to be tested by the target system, and for each target test point, a test requirement document exists. The test requirement document is a document preset by a system staff and containing test basic information, and a test requirement data list corresponding to the target test point can be extracted by analyzing the test requirement document. The non-public test cases refer to the cases designed for the target test points independently, the test points of different target systems are different, and the corresponding non-public test cases can be different. The non-public test case can be obtained by adjusting related data in the test case template according to the test requirement document. And the corresponding relation exists between the test requirement data list in the test requirement document and the related data in the test case template, and the related data in the test case template can be adjusted to the test requirement data list by determining the corresponding relation.
Optionally, the calling the target test template to execute the multiplexing test case may include:
determining target parameters in the target test template;
selecting a parameter value corresponding to the target parameter in the multiplexing test case;
and calling calculation logic in the target test template to execute the parameter value to obtain a system test expected value.
The real value obtaining module 204 may be configured to invoke the target system to execute the multiplexed test case to obtain a system test real value.
In at least one embodiment of the present application, the target system is called to execute the multiplexing test case, so as to obtain a system test actual value, and when the system test expected value and the system test actual value have large differences, it is determined that the target system has more defects and the system quality is poor by comparing the system test expected value and the system test actual value; and when the expected value of the system test is not different from the actual value of the system test, and the goods difference is small, the target system is determined to have fewer defects, and the system quality is better.
The curve fitting module 205 may be configured to perform curve fitting according to the expected values of the system test to obtain a first execution curve, and perform curve fitting according to the actual values of the system test to obtain a second execution curve.
In at least one embodiment of the present application, curve fitting is performed according to the expected value of the system test to obtain a first execution curve, and curve fitting is performed according to the actual value of the system test to obtain a second execution curve, so that the difference between the expected value of the system test and the actual value of the system test can be more intuitively seen by comparing the first execution curve with the second execution curve, thereby improving the quality monitoring efficiency of the system.
Optionally, performing curve fitting according to the expected values of the system test, and obtaining a first execution curve may include:
determining a preset function according to the expected value of the system test;
and calling the preset function to process the expected value of the system test to obtain a first execution curve.
The preset function is a preset function for determining a first execution curve. By adopting the method, curve fitting can be carried out according to the system test true value, and a second execution curve is obtained.
The difference calculation module 206 may be configured to calculate a degree of difference between the first execution curve and the second execution curve.
In at least one embodiment of the present application, calculating the difference between the first execution curve and the second execution curve may be accomplished by calculating the difference between trend data of two curves, where the trend data may refer to a slope of the corresponding execution curve.
Optionally, the calculating the degree of difference between the first execution curve and the second execution curve may include:
acquiring first trend data of the first execution curve in a preset stage;
acquiring second trend data of the second execution curve in the preset stage;
determining difference data between the first trend data and the second trend data;
and calculating the data quantity of the difference data, traversing the preset mapping relation between the data quantity and the difference degree according to the data quantity, and obtaining the difference degree corresponding to the data quantity.
The trend data may include a slope of a corresponding execution curve, and the preset phase may include three phases, which are respectively: smoking phase, SIT phase and regression phase. In the same phase, the slope variation ranges of the second execution curve and the first execution curve should be consistent. By dividing the execution curve according to the preset stage, the trend detection of the execution curve with the same slope change range is realized, and the monitoring efficiency of the execution curve is improved.
The quality determination module 207 may be configured to determine a system quality of the target system based on the degree of variance.
In at least one embodiment of the present application, the system quality may be sequentially classified into a class a, a class B and a class C according to the quality, where when the class of the system quality is a class a, it indicates that the system is defect-free, and the system quality is high; when the level of the system quality is B level, the system is indicated to have partial defects, and the system quality is medium; when the level of the system quality is C level, the system is indicated to have more defects and has low quality. For each level, there is a range of variability corresponding thereto. Determining the system quality of the target system according to the difference, namely determining a difference range corresponding to the target system according to the difference; and determining the system quality of the target system according to the difference degree range.
Referring to fig. 3, a schematic structural diagram of a computer device according to a third embodiment of the present application is shown. In the preferred embodiment of the present application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 3 is not limiting of the embodiments of the present application, and that either a bus-type configuration or a star-type configuration may be used, and that the computer device 3 may include more or less other hardware or software than that shown, or a different arrangement of components.
In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, mouse, remote control, touch pad, or voice control device, such as a personal computer, tablet, smart phone, digital camera, etc.
It should be noted that the computer device 3 is only used as an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application and are incorporated herein by reference.
In some embodiments, the memory 31 has stored therein a computer program which, when executed by the at least one processor 32, performs all or part of the steps of the system quality monitoring method as described. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects the various components of the entire computer device 3 using various interfaces and lines, and performs various functions and processes of the computer device 3 by running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or some of the steps of the system quality monitoring method described in embodiments of the present application; or to implement all or part of the functionality of the system quality monitoring device. The at least one processor 32 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.
In some embodiments, the at least one communication bus 33 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the computer device 3 may further comprise a power source (such as a battery) for powering the various components, preferably the power source is logically connected to the at least one processor 32 via a power management means, whereby the functions of managing charging, discharging, and power consumption are performed by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor (processor) to perform portions of the methods described in various embodiments of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. Several of the elements or devices recited in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above embodiments are merely for illustrating the technical solution of the present application and not for limiting, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application.
Claims (7)
1. A system quality monitoring method for quality monitoring of a target system, the system quality monitoring method comprising:
acquiring and analyzing preset information in a target system to obtain target parameters, and constructing an initial test template according to the target parameters;
determining computational logic between the target parameters, comprising: acquiring a target benefit item corresponding to preset information; splitting the target interest item to obtain a plurality of calculation factors; determining weight items and parameter items corresponding to the calculation factors; analyzing the parameter items to obtain calculation logic among all target parameters;
adjusting the initial test template according to the calculation logic to obtain a target test template, wherein the method comprises the following steps: acquiring calculation logic between target parameters corresponding to the target benefit items; establishing a data link between the target parameters according to the calculation logic; adding the data link in the initial test template to obtain a target test template;
Generating a multiplexed test case, comprising: acquiring a preset test case library, and selecting a public test case from the preset test case library; determining a target test point corresponding to the target system, and acquiring a test requirement document corresponding to the target test point; generating a non-public test case corresponding to the target test point according to the test requirement document and a preset test case template; combining the public test cases and the non-public test cases to obtain a multiplexing test case;
calling the target test template to execute the multiplexing test case to obtain a system test expected value;
calling the target system to execute the multiplexing test case to obtain a system test true value;
performing curve fitting according to the system test expected value to obtain a first execution curve, and performing curve fitting according to the system test true value to obtain a second execution curve;
calculating the difference degree between the first execution curve and the second execution curve;
and determining the system quality of the target system according to the difference degree.
2. The system quality monitoring method according to claim 1, wherein the obtaining and analyzing the preset information in the target system to obtain the target parameter includes:
Analyzing the preset information to obtain a target benefit item;
acquiring initial parameters corresponding to the target benefit items;
and screening and deleting repeated parameter values in the initial parameters to obtain target parameters.
3. The system quality monitoring method of claim 1, wherein constructing an initial test template from the target parameters comprises:
defining a parameter layout in an initial test template;
determining attribute values of the target parameters in the parameter layout;
and constructing an initial test template according to the parameter layout and the attribute values.
4. The system quality monitoring method of claim 1, wherein the calculating a degree of difference between the first execution curve and the second execution curve comprises:
acquiring first trend data of the first execution curve in a preset stage;
acquiring second trend data of the second execution curve in the preset stage;
determining difference data between the first trend data and the second trend data;
and calculating the data quantity of the difference data, traversing the preset mapping relation between the data quantity and the difference degree according to the data quantity, and obtaining the difference degree corresponding to the data quantity.
5. A system quality monitoring device, the system quality monitoring device comprising:
the parameter acquisition module is used for acquiring and analyzing preset information in a target system to obtain target parameters, and constructing an initial test template according to the target parameters;
the template adjustment module is used for determining calculation logic among the target parameters and comprises the following steps: acquiring a target benefit item corresponding to preset information; splitting the target interest item to obtain a plurality of calculation factors; determining weight items and parameter items corresponding to the calculation factors; analyzing the parameter items to obtain calculation logic among all target parameters;
the template adjustment module is further configured to adjust the initial test template according to the calculation logic to obtain a target test template, and includes: acquiring calculation logic between target parameters corresponding to the target benefit items; establishing a data link between the target parameters according to the calculation logic; adding the data link in the initial test template to obtain a target test template;
the expected value obtaining module is used for generating a multiplexing test case and comprises the following steps: acquiring a preset test case library, and selecting a public test case from the preset test case library; determining a target test point corresponding to the target system, and acquiring a test requirement document corresponding to the target test point; generating a non-public test case corresponding to the target test point according to the test requirement document and a preset test case template; combining the public test cases and the non-public test cases to obtain a multiplexing test case;
The expected value acquisition module is also used for calling the target test template to execute the multiplexing test case to obtain a system test expected value;
the true value acquisition module is used for calling the target system to execute the multiplexing test case to obtain a system test true value;
the curve fitting module is used for performing curve fitting according to the system test expected value to obtain a first execution curve, and performing curve fitting according to the system test true value to obtain a second execution curve;
the difference calculation module is used for calculating the difference degree between the first execution curve and the second execution curve;
and the quality determining module is used for determining the system quality of the target system according to the difference degree.
6. A computer device, characterized in that it comprises a processor for implementing the system quality monitoring method according to any one of claims 1 to 4 when executing a computer program stored in a memory.
7. A computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the system quality monitoring method according to any of claims 1 to 4.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111176979A (en) * | 2019-11-20 | 2020-05-19 | 四川蜀天梦图数据科技有限公司 | Test case generation method and device of graph database |
| CN111782541A (en) * | 2020-07-10 | 2020-10-16 | 泰康保险集团股份有限公司 | Test case generation method, device, equipment and computer readable storage medium |
| CN112115058A (en) * | 2020-09-25 | 2020-12-22 | 建信金融科技有限责任公司 | Test method and device, test case generation method and device and test system |
| WO2021003818A1 (en) * | 2019-07-08 | 2021-01-14 | 平安科技（深圳）有限公司 | Interface test case generating method and apparatus, computer device, and storage medium |
| CN112363923A (en) * | 2020-11-09 | 2021-02-12 | 中国平安人寿保险股份有限公司 | Test method, device, computer equipment and medium based on questionnaire system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7930597B2 (en) * | 2008-09-18 | 2011-04-19 | Alcatel-Lucent Usa Inc. | Method and apparatus for validating system properties exhibited in execution traces |
-
2021
- 2021-03-18 CN CN202110293131.3A patent/CN113010425B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021003818A1 (en) * | 2019-07-08 | 2021-01-14 | 平安科技（深圳）有限公司 | Interface test case generating method and apparatus, computer device, and storage medium |
| CN111176979A (en) * | 2019-11-20 | 2020-05-19 | 四川蜀天梦图数据科技有限公司 | Test case generation method and device of graph database |
| CN111782541A (en) * | 2020-07-10 | 2020-10-16 | 泰康保险集团股份有限公司 | Test case generation method, device, equipment and computer readable storage medium |
| CN112115058A (en) * | 2020-09-25 | 2020-12-22 | 建信金融科技有限责任公司 | Test method and device, test case generation method and device and test system |
| CN112363923A (en) * | 2020-11-09 | 2021-02-12 | 中国平安人寿保险股份有限公司 | Test method, device, computer equipment and medium based on questionnaire system |
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